Targeting and data collection techniques provide advertisers and other marketing organizations with market segment data related to advertising viewers, including, for example, computer users who view advertising on the World Wide Web (Web) or Internet. For advertising viewers such as Internet users, the available information related to each user depends, for example, on his or her historical Web behavior and, for example, on his or her origin environment, such as the user's computing platform, service provider, country, time of day, etc. A “market segment” or “segment” is a subset, or partial portion of a group that can be characterized in some way; a segment may also be a data object describing such a group.
Advertisers and other marketing organizations may create segment definitions to define groups of potential marketing targets (e.g., users) and direct advertising to those groups, such as groups of users on the Internet. “Data publishers” (or “data sellers”) may sell information concerning targets or people, such as Internet users, and their behaviors, which advertisers and other marketing organizations may use to create, for example, behavioral segment definitions. An Internet user may access a Web site of a data publisher, such as a bicycling interest Web site, for example, and be identified as a user “interested in bicycling”. Other attributes, such as time and location of the person's access, may also be identified. Data publishers may sell the identifying information about users who access their sites and receive income from sales based on this information's use.
User identification (ID) data from data publishers can be used to create segment definitions. In general, segment definitions may be characterized by specific values for available properties. For example, segment definitions might exist for categories such as “Gender”, “Age” and “Nationality” and one segment combination might be defined with three properties as, “Male, 35-40, European.” Once identified (e.g., from information from a data publisher (data seller)), a user who fits the characteristics of “Male, 35-40, European” can be grouped into and/or associated with this segment combination. An advertisement can be exposed to (or placed) with users identified with the segment combination, and data can be collected to determine how the users identified with that segment respond. Behavioral segment definitions for “Shopping Interest”, “Running Interest” and “Web surfing interest” can be defined and Behavioral attributes, such as “likes to shop”, “intensely likes running” or “Web surfs in the evening” can also be included in segment combinations. Segment combinations can have attributes that are purely behavioral, purely non-behavioral or a mixture of behavioral and non-behavioral.
The efficiency of a given advertisement depends on the match between the content of the advertisement (advertising content) and the market segment to which the content is exposed. In practice, a numeric “conversion ratio” value describes the efficiency or “success” relationship between the advertising content and target segment. A high conversion ratio value can show, for example, by various measures or various methods of determining or collecting such data, that a given advertisement or advertising campaign (group of advertisements) is well received by a given target segment.
It is perceived within the advertising and marketing industries that, in general, better and more accurate segment targeting capabilities could improve conversion ratios. High conversion ratios for advertisements, on the Internet and in other advertising venues, such as, e.g., print, outdoor, direct are desirable. Identification, for example, of a large user group with a high response rate to advertising and with members who respond in stable and predictable manners over time is desirable.
Within Internet marketing, serving systems for organizations executing advertisement placement in advertising campaigns may execute “media optimization” when placing an advertisement on a particular Web site or with a particular media publisher. Media optimization may include analyzing parameters in segment combinations to identify values for each parameter that may yield the “best results” for each advertisement the serving system runs. A serving system may be a networked computing system that enables an operator to place advertisements on particular Web pages. Serving systems place advertisements on behalf of an advertiser or advertising agency, and can be operated by a number of entities such as an independent operator working with an advertiser or advertising agency.
With the development of the Internet advertisement market, it is thought that bid based media purchasing may become more widely used. In such a system “real time bidding” (RTB) bidders acquire spaces from media publishers on a “real time bidding” (RTB) exchange. An RTB bidder in such a model could operate one or more serving systems. More accurate and efficient segmentation or grouping capabilities can provide an advantage to the bidders and allow them to more greatly maximize their profit in this arena. In such a bidding environment, response time becomes a critical element and it is perceived that automatic tools, including those with segment-identifying capabilities may improve operational efficiency.
Serving systems using available media optimization algorithms have distinct limitations. Automatic systems exist, but with such systems, segment content does not get improved with the time. Such systems, generally, use “raw” segment data as received from a data publisher (data seller). An algorithm for media optimization looking at such data can, for example, identify that a given creative works well (e.g. gets a high conversion ratio) when displayed to users on weekends. Based on that high weekend response, a media optimization using such live user data may show that the creative in question should be used mostly on weekends. This type of algorithm ignores past data, however, so, for example, the algorithm may not identify the fact that the campaign related to the creative works better for people who expressed an interest in technology gadgets within the last 30 days. Such an algorithm, for example, also does not allow for improvements as more data is amassed.
Another drawback in current systems is that such systems provide only limited centralization. A centralized system may, for example, allow handling of different data types, such as the different types of data provided by different types of data publishers (“data sellers”). Using behavioral aspects from a wide group of data publishers may provide an advantage in manipulating a wide variety of data and could enhance segment groupings. Such a centralized system, may also, when accessed by “data buyers”, further be able to improve the segment data over time and provide it in a manner that permits further automation of data buyer and data seller systems. Such automation may desirable for system such as an RTB media exchange system.
In general, there is a need for improved techniques for media optimization, in the advertising and marketing fields in general and, in particular, with regard to Internet advertising.